Covers the BD, BDe, BDeu, and K2 Bayesian scoring functions for Bayesian Belief Networks (BBNs)

1. BAYESIAN
SCORING
FUNCTIONS FOR
BAYESIAN BELIEF
NETWORKS (BBNS)
Jee Vang, vangjee@gmail.com
Version 3.1
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Unported License.

2. PURPOSE AND OUTLINE
 Purpose: concisely illustrate how some Bayesian scoring
functions have been established to score Bayesian belief
networks (BBNs)
 Define a BBN
 Cover what Bayesian scoring functions are based on
 Basic mathematic functions (factorial, gamma, and Beta functions)
 Probability distributions (multinomial, Dirichlet, Dirichlet-multinomial)
 Bayes’ Theorem
 Assumptions
 Give a few Bayesian scoring function examples (BD, K2, BDe,
BDeu)
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3. DEFINITION OF A BBN
 
A BBN is defined as a pair (G,P) where G and P themselves are defined as follows
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4. BASIC MATHEMATIC FUNCTIONS

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5. PROBABILITY DISTRIBUTIONS—
MULTINOMIAL AND DIRICHLET

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6. PROBABILITY DISTRIBUTIONS—
DIRICHLET-MULTINOMIAL

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7. PROBABILITY DISTRIBUTION—
DIRICHLET-MULTINOMIAL
(CONTINUED)

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8. PROBABILITY DISTRIBUTION—
DIRICHLET-MULTINOMIAL
(CONTINUED)

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9. PROBABILITY DISTRIBUTION—
DIRICHLET-MULTINOMIAL
(CONTINUED)

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10. BAYES’ THEOREM

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11. ASSUMPTIONS

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12. BAYESIAN DIRICHLET (BD) SCORING
FUNCTION

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13. SOME DIFFERENT BAYESIAN SCORING
FUNCTIONS

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14. SOME DIFFERENT BAYESIAN SCORING
FUNCTIONS (CONTINUED)

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15. SUMMARY

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